MétaCan
Menu
Back to cohort
Record W2106327554 · doi:10.2190/na.28.1.a

Determinants and Implications of Bone Grease Rendering: A Pacific Northwest Example

2007· article· en· W2106327554 on OpenAlexafffund
Paul Prince

Bibliographic record

VenueNorth American Archaeologist · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsMacEwan University
FundersSocial Sciences and Humanities Research Council of CanadaTrent University
KeywordsGreaseRendering (computer graphics)GeologyComputer scienceMaterials scienceArtificial intelligenceComposite material

Abstract

fetched live from OpenAlex

Highly fragmented, mainly indeterminate mammal bone from two sites on the inland margins of the Northwest Coast are analyzed in terms of size and fracture characteristics and interpreted as the result of bone grease rendering. Methodological approaches to the determination of this activity are discussed, and it is argued that multiple lines of evidence that include these characteristics in combination with interspecies comparisons of fragmentation levels and site processing facilities are most convincing. Predictions based on intra-skeletal grease yields, or utility indices, are not supported in this case, and similar arguments based on optimal behavior models which strictly relate grease rendering to resource stress are criticized. It is argued in this case that bone grease was routinely exploited as a supplement to the staple, dried salmon, and had advantages over the better known eulachon oil in this regard.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.308
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueNorth American ArchaeologistSame topicPleistocene-Era Hominins and ArchaeologyFrench-language works237,207